Milutin Milenković

Applying Terrestrial Laser Scanning for Soil Surface Roughness Assessment

Terrestrial laser scanning can provide high-resolution, two-dimensional sampling of soil surface roughness. While previous studies demonstrated the usefulness of these roughness measurements in geophysical applications, questions about the number of required scans and their resolution were not investigated thoroughly. Here, we suggest a method to generate digital elevation models, while preserving the surface’s stochastic properties at high frequencies and additionally providing an estimate of their spatial resolution. We also study the impact of the number and positions of scans on roughness indices’ estimates. An experiment over a smooth and isotropic soil plot accompanies the analysis, where scanning results are compared to results from active triangulation. The roughness measurement conditions for ideal sampling are revisited and updated for diffraction-limited sampling valid for close-range laser scanning over smooth and isotropic soil roughness. Our results show that terrestrial laser scanning can be readily used for roughness assessment on scales larger than 5 cm, while for smaller scales, special processing is required to mitigate the effect of the laser beam footprint. Interestingly, classical roughness parametrization (correlation length, root mean square height (RMSh)) was not sensitive to these effects. Furthermore, comparing the classical roughness parametrization between one- and four-scan setups shows that the one-scan data can replace the four-scan setup with a relative loss of accuracy below 1% for ranges up to 3 m and incidence angles no larger than 50°, while two opposite scans can replace it over the whole plot. The incidence angle limit for the spectral slope is even stronger and is 40°. These findings are valid for scanning over smooth and isotropic soil roughness.

Annual seasonality in Sentinel-1 signal for forest mapping and forest type classification

ABSTRACT The Sentinel-1 satellites provide the formerly unprecedented combination of high spatial and temporal resolution of dual polarization synthetic aperture radar data. The availability of dense time series enables the derivation and analysis of temporally filtered annual backscatter signals. The study concentrates on the use of Sentinel-1 seasonal backscatter signatures for forest area estimation and forest type classification. A classification method based on time series similarity measures is introduced and tested in three test areas covered by various forest types including broadleaf temperate, boreal and montane forests. The results are compared with two European-wide Copernicus high resolution layers, namely forest type and tree cover density (TCD). The correspondence of forest/non-forest maps and TCD is high in all test areas, with overall accuracies for forest/non-forest classification between 86% and 91% and Pearson correlation coefficients for TCD between 0.68 and 0.74. The forest type classification (non-forest, coniferous and broadleaf forest classes) provides best results in temperate forests with an overall accuracy of 85%; in boreal forest, the accuracy decreases to only 65%. Generally, the method provides reliable results for forest area estimation, including regions where methods based on static parameters are often problematic (mountainous areas), and it enables forest type classification in temperate forests.

Roughness Spectra Derived from Multi-Scale LiDAR Point Clouds of a Gravel Surface: A Comparison and Sensitivity Analysis

The roughness spectrum (i.e., the power spectral density) is a derivative of digital terrain models (DTMs) that is used as a surface roughness descriptor in many geomorphological and physical models. Although light detection and ranging (LiDAR) has become one of the main data sources for DTM calculation, it is still unknown how roughness spectra are affected when calculated from different LiDAR point clouds, or when they are processed differently. In this paper, we used three different LiDAR point clouds of a 1 m × 10 m gravel plot to derive and analyze the roughness spectra from the interpolated DTMs. The LiDAR point clouds were acquired using terrestrial laser scanning (TLS), and laser scanning from both an unmanned aerial vehicle (ULS) and an airplane (ALS). The corresponding roughness spectra are derived first as ensemble averaged periodograms and then the spectral differences are analyzed with a dB threshold that is based on the 95% confidence intervals of the periodograms. The aim is to determine scales (spatial wavelengths) over which the analyzed spectra can be used interchangeably. The results show that one TLS scan can measure the roughness spectra for wavelengths larger than 1 cm (i.e., two times its footprint size) and up to 10 m, with spectral differences less than 0.65 dB. For the same dB threshold, the ULS and TLS spectra can be used interchangeably for wavelengths larger than about 1.2 dm (i.e., five times the ULS footprint size). However, the interpolation parameters should be optimized to make the ULS spectrum more accurate at wavelengths smaller than 1 m. The plot size was, however, too small to draw particular conclusions about ALS spectra. These results show that novel ULS data has a high potential to replace TLS for roughness spectrum calculation in many applications.